{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f2570953-a56e-4bb1-ace6-5e70f2f7b329",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "874da299-6465-4c92-aa37-99c1d8b6fd11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a7d20653-24db-4243-9641-fc07196b9156",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabet_labels = \"A B C D E F G H I J K L M N O P Q R S T U V W X Y Z\".split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aaa1f76e-db3f-481e-8666-615feea05189",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path(\"alphabet/Images/Images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2d69433f-925a-46d2-927c-67b02eb2f896",
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_font(n=0):\n",
    "    return [load_image(path/f\"{c}/{n}.png\", mode=\"L\") for c in alphabet_labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5ed0dc36-06df-44f5-930d-ad3d294ee4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mload_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "\u001b[0;32mdef\u001b[0m \u001b[0mload_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"Open and load a `PIL.Image` and convert to `mode`\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      ~/mambaforge/envs/fastai/lib/python3.10/site-packages/fastai/vision/core.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_image??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6065c04d-51e6-402f-af6c-47669de71231",
   "metadata": {},
   "outputs": [],
   "source": [
    "@delegates(subplots)\n",
    "def show_images(ims, nrows=1, ncols=None, titles=None, show_args={}, **kwargs):\n",
    "    \"Show all images `ims` as subplots with `rows` using `titles`.\"\n",
    "    if ncols is None: ncols = int(math.ceil(len(ims)/nrows))\n",
    "    if titles is None: titles = [None]*len(ims)\n",
    "    axs = subplots(nrows, ncols, **kwargs)[1].flat\n",
    "    for im,t,ax in zip(ims, titles, axs): show_image(im, ax=ax, title=t, **show_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5b7efab9-9fbb-4e04-ae75-26d4dcda1872",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 7800x300 with 26 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7800x300 with 26 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7800x300 with 26 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7800x300 with 26 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7800x300 with 26 Axes>"
      ]
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    }
   ],
   "source": [
    "for n in random.sample(range(1000), k=5):\n",
    "    show_images(open_font(n), show_args={'cmap':'Greys'})"
   ]
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   "execution_count": 59,
   "id": "7961d6e9-85a3-412c-b0ce-9abb808877a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open_font()"
   ]
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   "execution_count": 60,
   "id": "ed5ea86c-6e8e-40aa-bdce-6db2921c288c",
   "metadata": {},
   "outputs": [
    {
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       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(f[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c2db937a-33c1-47c4-bd3a-0fe9ef80cb71",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wandb_bw_image(pil_image):\n",
    "    return wandb.Image(255 - np.array(pil_image))"
   ]
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  {
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   "execution_count": 80,
   "id": "7423c747-e5c6-4c4e-b358-9b3a52c52e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = wandb.Table(columns=[\"id\", *alphabet_labels])\n",
    "for n in random.sample(range(1000), k=100):\n",
    "    font = open_font(n)\n",
    "    table.add_data(n, *[wandb_bw_image(c) for c in font])"
   ]
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   "execution_count": 81,
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   "outputs": [
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       "Tracking run with wandb version 0.13.2"
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    {
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